Triple
T6843632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huangshan (city) |
E157836
|
entity |
| Predicate | hasFormerName |
P65
|
FINISHED |
| Object | Huizhou (徽州) |
E157836
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Huizhou (徽州) | Statement: [Huangshan (city), hasFormerName, Huizhou (徽州)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huizhou (徽州) Context triple: [Huangshan (city), hasFormerName, Huizhou (徽州)]
-
A.
六安
六安 is a prefecture-level city in western Anhui Province, China, known for its rich history and famous Lu'an Melon Seed tea.
-
B.
Huangshan (city)
chosen
Huangshan is a scenic city in southern Anhui Province, China, best known as the gateway to the famous Yellow Mountain (Huangshan) range and its surrounding cultural and natural heritage sites.
-
C.
Suzhou (Anhui)
Suzhou (Anhui) is a county-level city in northern Anhui Province, China, known as an important regional center for agriculture and light industry.
-
D.
Xuancheng
Xuancheng is a county-level city in southeastern Anhui Province, China, known for its historical heritage and traditional Chinese ink production.
-
E.
马鞍山
马鞍山是位于中国安徽省东部、长江沿岸的一座以钢铁工业和山水景观著称的地级市。
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c6882ed4c081909dc465a7cf8838be |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d6b7179481909e3482fef47b2719 |
completed | March 27, 2026, 7:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72fbf06008190a8c342d3d7dec930 |
completed | March 28, 2026, 1:32 a.m. |
Created at: March 27, 2026, 2:19 p.m.